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Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

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Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors. / Schirmer, Pascal; Mporas, Iosif; Sheikh-Akbari, Akbar .

In: Energies, Vol. 13, No. 9, 2148, 01.05.2020.

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Schirmer, Pascal ; Mporas, Iosif ; Sheikh-Akbari, Akbar . / Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors. In: Energies. 2020 ; Vol. 13, No. 9.

Bibtex

@article{61c84cf5dda74893893ed948cbd912c0,
title = "Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors",
abstract = "A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.",
author = "Pascal Schirmer and Iosif Mporas and Akbar Sheikh-Akbari",
note = "{\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).",
year = "2020",
month = may,
day = "1",
doi = "10.3390/en13092148",
language = "English",
volume = "13",
journal = "Energies",
issn = "1996-1073",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "9",

}

RIS

TY - JOUR

T1 - Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

AU - Schirmer, Pascal

AU - Mporas, Iosif

AU - Sheikh-Akbari, Akbar

N1 - © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

PY - 2020/5/1

Y1 - 2020/5/1

N2 - A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

AB - A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

U2 - 10.3390/en13092148

DO - 10.3390/en13092148

M3 - Article

VL - 13

JO - Energies

JF - Energies

SN - 1996-1073

IS - 9

M1 - 2148

ER -